Practice variation in anti-epileptic drug use for neonatal hypoxic-ischemic encephalopathy among regional NICUs
Bibliographic record
Abstract
BACKGROUND: While intercenter variation (ICV) in anti-epileptic drug (AED) use in neonates with seizures has been previously reported, variation in AED practices across regional NICUs has not been specifically and systematically evaluated. This is important as these centers typically have multidisciplinary neonatal neurocritical care teams and protocolized approaches to treating conditions such as hypoxic ischemic encephalopathy (HIE), a population at high risk for neonatal seizures. To identify opportunities for quality improvement (QI), we evaluated ICV in AED utilization for neonates with HIE treated with therapeutic hypothermia (TH) across regional NICUs in the US. METHODS: Children's Hospital Neonatal Database and Pediatric Health Information Systems data were linked for 1658 neonates ≥36 weeks' gestation, > 1800 g birthweight, with HIE treated with TH, from 20 NICUs, between 2010 and 2016. ICV in AED use was evaluated using a mixed-effect regression model. Rates of AED exposure, duration, prescription at discharge and standardized AED costs per patient were calculated as different measures of utilization. RESULTS: Ninety-five percent (range: 83-100%) of patients with electrographic seizures, and 26% (0-81%) without electrographic seizures, received AEDs. Phenobarbital was most frequently used (97.6%), followed by levetiracetam (16.9%), phenytoin/fosphenytoin (15.6%) and others (2.4%; oxcarbazepine, topiramate and valproate). There was significant ICV in all measures of AED utilization. Median cost of AEDs per patient was $89.90 (IQR $24.52,$258.58). CONCLUSIONS: Amongst Children's Hospitals, there is marked ICV in AED utilization for neonatal HIE. Variation was particularly notable for HIE patients without electrographic seizures, indicating that this population may be an appropriate target for QI processes to harmonize neuromonitoring and AED practices across centers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".